sarthaksharma39 / time_series_forecasting

To develop an advance forecasting model that adeptly incorporates solar irradiance data, leveraging its predictive capabilities to elevate forecasting performance and reliability.

Time_series_forecasting

A HYBRID SYSTEM BASED ON ENSEMBLE LEARNING FOR TIME SERIES FORECASTING OF SOLAR IRRADIANCE

TASK ->

A approach that combines different method (hybrid system) and ensemble learning technique to create a model that specifically focus on improving the accuracy of the time-series forecasting by effectively predicting the residuals.

Time-series forecating ->

Predicting future value based on historical data organized in a time-dependant sequence.

Ensemble learning ->

It involve combining multiple model to create a stronger, more robust model.

Impact:

Advance renewable energy optimization and atmospheric science

Problem Statement:

Develop a hybrid forecasting system that leverages ensemble learning techniques to improve the performance of time series forecasting of solar irradiance by effectively modeling meteorological parameters.

Future scope:

Integrate AOD (Aerosol Optical Depth) data with other meteorological parameters can enhance time series prediction of solar irradiance.

For more details check the uploaded presentation